The conditional operator is supported in many programming languages. This term usually refers to ?: as in C, C++, C#, and JavaScript. However, in Java, this term can also refer to && and ||. && and || In some programming languages, e.g. Java, the term conditional operator refers to short circuit boolean operators && and ||. The second expression is evaluated only when the first expression is not sufficient to determine the value of the whole expression. Difference from bitwise operator & and | are bitwise operators that occur in many programming languages. The major difference is that bitwise operations operate on the individual bits of a binary numeral, whereas conditional operators operate on logical operations. Additionally, expressions before and after a bitwise operator are always evaluated. If expression 1 is true, expressions 2 and 3 are NOT checked. This checks expressions...

Test(s), testing, or TEST may refer to: Test (assessment), an educational assessment intended to measure the respondents' knowledge or other abilities Arts and entertainment Test (2013 film), an American film Test (2014 film), a Russian film Test (group), a jazz collective Tests (album), a 1998 album by The MicrophonesComputing .test, a reserved top-level domain test (Unix), a Unix command for evaluating conditional expressions TEST (x86 instruction), an x86 assembly language instructionPeople Test (wrestler), ring name for Andrew Martin (1975–2009), Canadian professional wrestler John Test (1771–1849), American politician Zack Test (born 1989), American rugby union playerScience and technology Proof test Stress testing Test (biology), the shell of sea urchins and certain microorganisms Test...

We give a consistent quantum description of time, based on Page and Wootters' conditional probabilities mechanism, that overcomes the criticisms that were raised against similar previous proposals. In particular we show how the model allows to reproduce the correct statistics of sequential measurements performed on a system at different times.

We study the computational complexity of quantum discord (a measure of quantum correlation beyond entanglement), and prove that computing quantum discord is NP-complete. Therefore, quantum discord is computationally intractable: the running time of any algorithm for computing quantum discord is believed to grow exponentially with the dimension of the Hilbert space so that computing quantum discord in a quantum system of moderate size is not possible in practice. As by-products, some entanglement measures (namely entanglement cost, entanglement of formation, relative entropy of entanglement, squashed entanglement, classical squashed entanglement, conditional entanglement of mutual information, and broadcast regularization of mutual information) and constrained Holevo capacity are NP-hard/NP-complete to compute. These complexity-theoretic results are directly applicable in common randomness distillation, quantum state merging, entanglement distillation, superdense coding, and quantum teleportation; they may offer significant insights into quantum information processing. Moreover, we prove the NP-completeness of two typical problems: linear optimization over classical states and detecting classical states in a convex set, providing evidence that working with classical states is generically computationally intractable.

A new method is proposed for geostatistical simulation and reconstruction of porous media by integrating hard (quantitative) and soft (qualitative) data with a newly developed method of reconstruction. The reconstruction method is based on a cross-correlation function that we recently proposed and contains global multiple-point information about the porous medium under study, which is referred to cross-correlation-based simulation (CCSIM). The porous medium to be reconstructed is represented by a reference image (RI). Some of the information contained in the RI is represented by a training image (TI). In unconditional simulation, only the TI is used to reconstruct the RI, without honoring any particular data. If some soft data, such as a seismic image, and hard data are also available, they are integrated with the TI and conditional CCSIM method in order to reconstruct the RI, by honoring the hard data exactly. To illustrate the method, several two- and three-dimensional porous media are simulated and reconstructed, and the results are compared with those provided by the RI, as well as those generated by the traditional two-point geostatistical simulation, namely the co-sequential Gaussian simulation. To quantify the accuracy of the simulations and reconstruction, several statistical properties of the porous media, such as their porosity distribution, variograms, and long-range connectivity, as well as two-phase flow of oil and water through them, are computed. Excellent agreement is demonstrated between the results computed with the simulated model and those obtained with the RI.

Toronto traded the rights to German forward Prince Owusu to rival Montreal on Monday in exchange for $100,000 in 2025 general allocation money and $75,000 in 2026 GAM for the right of first refusal for Owusu. The deal also includes$75,000 in conditional allocation money.

Bound states of massive particles, such as nuclei, atoms or molecules, constitute the bulk of the visible world around us. In contrast, photons typically only interact weakly. We report the observation of traveling three-photon bound states in a quantum nonlinear medium where the interactions between photons are mediated by atomic Rydberg states. Photon correlation and conditional phase measurements reveal the distinct bunching and phase features associated with three-photon and two-photon bound states. Such photonic trimers and dimers possess shape-preserving wavefunctions that depend on the constituent photon number. The observed bunching and strongly nonlinear optical phase are quantitatively described by an effective field theory (EFT) of Rydberg-induced photon-photon interactions, consistent with the presence of a substantial effective three-body force between the photons. These observations demonstrate the ability to realize and control strongly interacting quantum many-body states of light.

Mutual information (MI) quantifies the statistical dependency between a pair of random variables, and plays a central role in the analysis of engineering and biological systems. Estimation of MI is difficult due to its dependence on an entire joint distribution, which is difficult to estimate from samples. Here we discuss several regularized estimators for MI that employ priors based on the Dirichlet distribution. First, we discuss three "quasi-Bayesian" estimators that result from linear combinations of Bayesian estimates for conditional and marginal entropies. We show that these estimators are not in fact Bayesian, and do not arise from a well-defined posterior distribution and may in fact be negative. Second, we show that a fully Bayesian MI estimator proposed by Hutter (2002), which relies on a fixed Dirichlet prior, exhibits strong prior dependence and has large bias for small datasets. Third, we formulate a novel Bayesian estimator using a mixture-of-Dirichlets prior, with mixing weights designed to produce an approximately flat prior over MI. We examine the performance of these estimators with a variety of simulated datasets and show that, surprisingly, quasi-Bayesian estimators generally outperform our Bayesian estimator. We discuss outstanding challenges for MI estimation and suggest promising avenues for future research.

The small-scale environment characterized by the local density is known to play a crucial role in deciding the galaxy properties but the role of large-scale environment on galaxy formation and evolution still remain a less clear issue. We propose an information theoretic framework to investigate the influence of large-scale environment on galaxy properties and apply it to the data from the Galaxy Zoo project which provides the visual morphological classifications of $sim 1$ million galaxies from the Sloan Digital Sky Survey. We find a non-zero mutual information between morphology and environment which decreases with increasing length scales but persists throughout the entire length scales probed. We estimate the conditional mutual information and the interaction information between morphology and environment by conditioning the environment on different length scales and find a synergic interaction between them which operates upto at least a length scales of $ sim 30 , h^{-1}, {rm Mpc}$. Our analysis indicates that these interactions largely arise due to the mutual information shared between the environments on different length scales.

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.

In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished, following Jebara (2004): A generative model is a statistical model of the joint probability distribution P ( X , Y ) {displaystyle P(X,Y)} on a given observable variable X and target variable Y; A generative model can be used to "generate" random instances (outcomes) of an observation x. A discriminative model is a model of the conditional probability ...